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Condorcet fusion for improved retrieval
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Source Conference on Information and Knowledge Management archive
Proceedings of the eleventh international conference on Information and knowledge management table of contents
McLean, Virginia, USA
SESSION: Information retrieval table of contents
Pages: 538 - 548  
Year of Publication: 2002
ISBN:1-58113-492-4
Authors
Mark Montague  Dartmouth College, Hanover, NH
Javed A. Aslam  Dartmouth College, Hanover, NH
Sponsors
SIGMIS: ACM Special Interest Group on Management Information Systems
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 63,   Citation Count: 31
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ABSTRACT

We present a new algorithm for improving retrieval results by combining document ranking functions: Condorcet-fuse. Beginning with one of the two major classes of voting procedures from Social Choice Theory, the Condorcet procedure, we apply a graph-theoretic analysis that yields a sorting-based algorithm that is elegant, efficient, and effective. The algorithm performs very well on TREC data, often outperforming existing metasearch algorithms whether or not relevance scores and training data is available. Condorcet-fuse significantly outperforms Borda-fuse, the analogous representative from the other major class of voting algorithms.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
 
3
 
4
N. Belkin, P. Kantor, C. Cool, and R. Quatrain. Combining evidence for information retrieval. In Harman {15}, pages 35--43.
 
5
N. Craswell, D. Hawking, and P. Thistlewaite. Merging results from isolated search engines. In Proceedings of the Tenth Australasian Database Conference, Aukland, New Zealand, Jan. 1999. Springer-Verlag.
 
6
W. B. Croft. Combining approaches to information retrieval. In W. B. Croft, editor, Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, The Kluwer International Series on Information Retrieval, chapter~1. Kluwer Academic Publishers, 2000.
 
7
W. B. Croft, D. J. Harper, D. H. Kraft, and J. Zobel, editors. SIGIR'01, Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, Sept. 2001. ACM Press, New York.
 
8
J. C. de~Borda. Mémoire sur les élections au scrutin. In Histoire de l'Academie Royale des Sciences. Paris, 1781.
 
9
M. de~Condorcet. Essai sur l'application de l'analyse à la probabilité des decisions rendues à la pluralité des voix, 1785.
 
10
H. L. Fisher and D. R. Elchesen. Effectiveness of combining title words and index terms in machine retrieval searches. Nature, 238:109--110, July 1972.
11
 
12
E. A. Fox, M. P. Koushik, J. Shaw, R. Modlin, and D. Rao. Combining evidence from multiple searches. In D. Harman, editor, The First Text REtrieval Conference (TREC-1), pages 319--328, Gaithersburg, MD, USA, Mar. 1993. U.S. Government Printing Office, Washington D.C.
 
13
E. A. Fox and J. A. Shaw. Combination of multiple searches. In Harman {15}, pages 243--249.
 
14
 
15
D. Harman, editor. The Second Text REtrieval Conference (TREC-2), Gaithersburg, MD, USA, Mar. 1994. U.S. Government Printing Office, Washington D.C.
16
 
17
J. S. Kelly. Social Choice Theory: An Introduction. Springer-Verlag, 1988.
18
19
20
 
21
H. Moulin. Axioms of Cooperative Decision Making. Cambridge University Press, 1988.
 
22
K. B. Ng. An Investigation of the Conditions for Effective Data Fusion in Information Retrieval. PhD thesis, School of Communication, Information, and Library Studies, Rutgers University, 1998.
 
23
K. B. Ng and P. B. Kantor. An investigation of the preconditions for effective data fusion in IR: A pilot study. In Proceedings of the 61th Annual Meeting of the American Society for Information Science, 1998.
 
24
K. B. Ng, D. Loewenstern, C. Basu, H. Hirsh, and P. B. Kantor. Data fusion of machine learning methods for the TREC5 routing task (and other work). In Voorhees and Harman {35}, pages 477--487.
 
25
W. H. Riker. Liberalism Against Populism. Waveland Press, 1982.
 
26
 
27
J. A. Shaw and E. A. Fox. Combination of multiple searches. In D. Harman, editor, Overview of the Third Text REtrieval Conference (TREC-3), pages 105--108, Gaithersburg, MD, USA, Apr. 1995. U.S. Government Printing Office, Washington D.C.
 
28
 
29
 
30
 
31
 
32
C. C. Vogt. How much more is better? Characterizing the effects of adding more IR systems to a combination. In Content-Based Multimedia Information Access (RIAO), pages 457--475, Paris, France, Apr. 2000.
 
33
 
34
C. C. Vogt, G. W. Cottrell, R. K. Belew, and B. T. Bartell. Using relevance to train a linear mixture of experts. In Voorhees and Harman {35}, pages 503--515.
 
35
E. Voorhees and D. Harman, editors. The Fifth Text REtrieval Conference (TREC-5), Gaithersburg, MD, USA, 1997. U.S. Government Printing Office, Washington D.C.
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CITED BY  31
Collaborative Colleagues:
Mark Montague: colleagues
Javed A. Aslam: colleagues